Lockman SWIRE master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
255270d (Fri Nov 24 10:35:51 2017 +0000)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20170710"

master_catalogue_filename = "master_catalogue_lockman-swire_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))

Diagnostics done using: master_catalogue_lockman-swire_20171128.fits

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn3(
    [
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 6),
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 5),
        np.sum(flag_obs == 3),
        np.sum(flag_obs == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn3(
    [
        np.sum(flag_det[flag_obs == 7] == 4),
        np.sum(flag_det[flag_obs == 7] == 2),
        np.sum(flag_det[flag_obs == 7] == 6),
        np.sum(flag_det[flag_obs == 7] == 1),
        np.sum(flag_det[flag_obs == 7] == 5),
        np.sum(flag_det[flag_obs == 7] == 3),
        np.sum(flag_det[flag_obs == 7] == 7)
    ],
    set_labels=('mid-IR', 'near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = ["WFC u",          "CFHT Megacam u"]
g_bands = ["WFC g", "RCS g", "CFHT Megacam g",  "GPC1 g"]
r_bands = ["WFC r", "RCS r", "CFHT Megacam r",  "GPC1 r"]
i_bands = ["WFC i", "RCS i",                    "GPC1 i"]
z_bands = ["WFC z", "RCS z", "CFHT Megacam z",  "GPC1 z"]
y_bands = [         "RCS y",                    "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

CFHT Megacam u (aperture) - WFC u (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.25
- 1% percentile: -0.9491949844360351
- 99% percentile: 3.2663552665710673

CFHT Megacam u (total) - WFC u (total):
- Median: -0.27
- Median Absolute Deviation: 0.21
- 1% percentile: -1.6218692207336425
- 99% percentile: 2.0539311504363935

No sources have both WFC g (aperture) and RCS g (aperture) values.
RCS g (total) - WFC g (total):
- Median: -0.08
- Median Absolute Deviation: 0.18
- 1% percentile: -0.9441648864746093
- 99% percentile: 1.156666412353516

CFHT Megacam g (aperture) - WFC g (aperture):
- Median: 0.11
- Median Absolute Deviation: 0.14
- 1% percentile: -0.5078629302978516
- 99% percentile: 0.8724484062194824

CFHT Megacam g (total) - WFC g (total):
- Median: -0.04
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8757493972778321
- 99% percentile: 1.4329431533813466

GPC1 g (aperture) - WFC g (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.27
- 1% percentile: -2.033579864501953
- 99% percentile: 2.2427005767822266

GPC1 g (total) - WFC g (total):
- Median: -0.03
- Median Absolute Deviation: 0.24
- 1% percentile: -2.5378850173950194
- 99% percentile: 1.7712992477417002

No sources have both RCS g (aperture) and CFHT Megacam g (aperture) values.
CFHT Megacam g (total) - RCS g (total):
- Median: 0.05
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0496853256225587
- 99% percentile: 1.2588200759887684

No sources have both RCS g (aperture) and GPC1 g (aperture) values.
GPC1 g (total) - RCS g (total):
- Median: 0.04
- Median Absolute Deviation: 0.31
- 1% percentile: -3.0529003143310547
- 99% percentile: 1.9456966972351046

GPC1 g (aperture) - CFHT Megacam g (aperture):
- Median: -0.33
- Median Absolute Deviation: 0.38
- 1% percentile: -2.9429248237609866
- 99% percentile: 2.1852089309692397

GPC1 g (total) - CFHT Megacam g (total):
- Median: 0.01
- Median Absolute Deviation: 0.27
- 1% percentile: -3.230147476196289
- 99% percentile: 1.8613366317748992

No sources have both WFC r (aperture) and RCS r (aperture) values.
RCS r (total) - WFC r (total):
- Median: -0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0184492111206054
- 99% percentile: 1.12237449645996

CFHT Megacam r (aperture) - WFC r (aperture):
- Median: 0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -0.545387954711914
- 99% percentile: 1.148755531311045

CFHT Megacam r (total) - WFC r (total):
- Median: -0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.9593395805358886
- 99% percentile: 1.6030628204345634

GPC1 r (aperture) - WFC r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.2963315582275392
- 99% percentile: 1.1390761566162118

GPC1 r (total) - WFC r (total):
- Median: -0.01
- Median Absolute Deviation: 0.14
- 1% percentile: -1.7165836334228515
- 99% percentile: 0.9766998291015625

No sources have both RCS r (aperture) and CFHT Megacam r (aperture) values.
CFHT Megacam r (total) - RCS r (total):
- Median: 0.07
- Median Absolute Deviation: 0.17
- 1% percentile: -1.103610725402832
- 99% percentile: 1.3413280487060542

No sources have both RCS r (aperture) and GPC1 r (aperture) values.
GPC1 r (total) - RCS r (total):
- Median: 0.13
- Median Absolute Deviation: 0.14
- 1% percentile: -1.851234474182129
- 99% percentile: 1.0487995147705078

GPC1 r (aperture) - CFHT Megacam r (aperture):
- Median: -0.26
- Median Absolute Deviation: 0.25
- 1% percentile: -2.3193286895751952
- 99% percentile: 1.125625000000003

GPC1 r (total) - CFHT Megacam r (total):
- Median: 0.07
- Median Absolute Deviation: 0.14
- 1% percentile: -2.370811748504639
- 99% percentile: 1.0244120407104438

No sources have both WFC i (aperture) and RCS i (aperture) values.
RCS i (total) - WFC i (total):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9779530334472656
- 99% percentile: 1.4092513275146472

GPC1 i (aperture) - WFC i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.14
- 1% percentile: -1.025598430633545
- 99% percentile: 0.9575976371765118

GPC1 i (total) - WFC i (total):
- Median: 0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -1.102666082382202
- 99% percentile: 0.9996103668212877

No sources have both RCS i (aperture) and GPC1 i (aperture) values.
GPC1 i (total) - RCS i (total):
- Median: 0.17
- Median Absolute Deviation: 0.12
- 1% percentile: -1.615425796508789
- 99% percentile: 0.8306423950195325

No sources have both WFC z (aperture) and RCS z (aperture) values.
RCS z (total) - WFC z (total):
- Median: -0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -0.999578857421875
- 99% percentile: 1.782779693603516

CFHT Megacam z (aperture) - WFC z (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.17
- 1% percentile: -0.6397380828857422
- 99% percentile: 3.043675384521484

CFHT Megacam z (total) - WFC z (total):
- Median: -0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.9713739395141602
- 99% percentile: 2.110036201477051

GPC1 z (aperture) - WFC z (aperture):
- Median: 0.01
- Median Absolute Deviation: 0.15
- 1% percentile: -0.9230419921874999
- 99% percentile: 1.1071548843383785

GPC1 z (total) - WFC z (total):
- Median: 0.09
- Median Absolute Deviation: 0.19
- 1% percentile: -0.9829817771911621
- 99% percentile: 1.4533742713928244

No sources have both RCS z (aperture) and CFHT Megacam z (aperture) values.
CFHT Megacam z (total) - RCS z (total):
- Median: 0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.255912399291992
- 99% percentile: 2.259424972534189

No sources have both RCS z (aperture) and GPC1 z (aperture) values.
GPC1 z (total) - RCS z (total):
- Median: 0.15
- Median Absolute Deviation: 0.18
- 1% percentile: -1.9935058212280274
- 99% percentile: 1.3433990478515625

GPC1 z (aperture) - CFHT Megacam z (aperture):
- Median: -0.22
- Median Absolute Deviation: 0.26
- 1% percentile: -2.777841310501098
- 99% percentile: 1.4052535629272451

GPC1 z (total) - CFHT Megacam z (total):
- Median: 0.12
- Median Absolute Deviation: 0.18
- 1% percentile: -2.3167495727539062
- 99% percentile: 1.362018585205071

No sources have both RCS y (aperture) and GPC1 y (aperture) values.
GPC1 y (total) - RCS y (total):
- Median: -0.57
- Median Absolute Deviation: 0.41
- 1% percentile: -3.220444412231445
- 99% percentile: 1.1729692840576167

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Lockman-SWIRE.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

WFC u (total) - SDSS u (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

CFHT Megacam u (aperture) - SDSS u (fiberMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7239515686035158
- 99% percentile: 3.037174682617188

CFHT Megacam u (total) - SDSS u (petroMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7239515686035158
- 99% percentile: 3.037174682617188

WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.153176498413086
- 99% percentile: 0.4261890411376952

WFC g (total) - SDSS g (petroMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.153176498413086
- 99% percentile: 0.4261890411376952

No sources have both SDSS g (fiberMag) and RCS g (aperture) values.
No sources have both SDSS g (petroMag) and RCS g (total) values.
CFHT Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0998111724853517
- 99% percentile: 1.262142181396477

CFHT Megacam g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0998111724853517
- 99% percentile: 1.262142181396477

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.496896743774414
- 99% percentile: 1.7368240356445312

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.56
- Median Absolute Deviation: 0.26
- 1% percentile: -2.496896743774414
- 99% percentile: 1.7368240356445312

WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245659255981445
- 99% percentile: 0.3214846229553226

WFC r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245659255981445
- 99% percentile: 0.3214846229553226

No sources have both SDSS r (fiberMag) and RCS r (aperture) values.
No sources have both SDSS r (petroMag) and RCS r (total) values.
CFHT Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8575413894653321
- 99% percentile: 2.322139625549326

CFHT Megacam r (total) - SDSS r (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8575413894653321
- 99% percentile: 2.322139625549326

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5779302787780762
- 99% percentile: 0.6094411659240673

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.43
- Median Absolute Deviation: 0.14
- 1% percentile: -1.5779302787780762
- 99% percentile: 0.6094411659240673

WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962218856811523
- 99% percentile: 0.35475969314575084

WFC i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962218856811523
- 99% percentile: 0.35475969314575084

No sources have both SDSS i (fiberMag) and RCS i (aperture) values.
No sources have both SDSS i (petroMag) and RCS i (total) values.
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1483933067321777
- 99% percentile: 0.3729721450805654

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1483933067321777
- 99% percentile: 0.3729721450805654

WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

WFC z (total) - SDSS z (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

No sources have both SDSS z (fiberMag) and RCS z (aperture) values.
No sources have both SDSS z (petroMag) and RCS z (total) values.
CFHT Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.14
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1127138900756837
- 99% percentile: 2.7650779533386163

CFHT Megacam z (total) - SDSS z (petroMag):
- Median: -0.14
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1127138900756837
- 99% percentile: 2.7650779533386163

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4175505828857422
- 99% percentile: 1.0491403579711862

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.17
- 1% percentile: -1.4175505828857422
- 99% percentile: 1.0491403579711862

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_Lockman-SWIRE.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_wfcam_j'][ml_twomass_idx],
                labels=("2MASS J", "WFCAM J (total)"))

WFCAM J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.05
- 1% percentile: -0.9130657836820437
- 99% percentile: 0.575751805124098

wfcam_ks_like = master_catalogue['m_wfcam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, wfcam_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "WFCAM Ks-like (total)"))

WFCAM Ks-like (total) - 2MASS Ks:
- Median: 0.07
- Median Absolute Deviation: 0.08
- 1% percentile: -0.8240238957842398
- 99% percentile: 0.9100533730451055

Keeping only sources with good signal to noise ratio

From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.

To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.

for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
    column = error_column.replace("merr", "m")
    keep_mask = np.isfinite(master_catalogue[error_column])
    keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
    master_catalogue[column][~keep_mask] = np.nan

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_cfht_megacam_r'],
    master_catalogue['m_ap_cfht_megacam_r'] - master_catalogue['m_cfht_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 2456857 / 4366332 (56.27%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    "g - i (WFC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 85805 / 4366332 (1.97%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_irac_i1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 129379 / 4366332 (2.96%)

nb_ccplots(
    master_catalogue['m_cfht_megacam_u'] - master_catalogue['m_cfht_megacam_g'],
    master_catalogue['m_cfht_megacam_g'] - master_catalogue['m_cfht_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)

Number of source used: 2070789 / 4366332 (47.43%)

nb_ccplots(
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfcam_j'],
    "J - K (WFCAM)", "g - J (WFC, WFCAM)",
    master_catalogue["stellarity"]
)

Number of source used: 108739 / 4366332 (2.49%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_wfc_z'],
    master_catalogue['m_wfc_z'] - master_catalogue['m_wfcam_j'],
    "i - z (WFC)", "z - J (WFC, WFCAM)",
    master_catalogue["stellarity"]
)

Number of source used: 11038 / 4366332 (0.25%)

nb_ccplots(
    master_catalogue['m_irac_i3'] - master_catalogue['m_irac_i4'],
    master_catalogue['m_irac_i1'] - master_catalogue['m_irac_i2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)

Number of source used: 38805 / 4366332 (0.89%)